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Aktius, Malin
Publications (3 of 3) Show all publications
Aktius, M. & Ziemke, T. (2012). Kognitiv robotik (1ed.). In: Jens Allwood, Mikael Jensen (Ed.), Kognitionsvetenskap: (pp. 551-560). Studentlitteratur
Open this publication in new window or tab >>Kognitiv robotik
2012 (Swedish)In: Kognitionsvetenskap / [ed] Jens Allwood, Mikael Jensen, Studentlitteratur, 2012, 1, p. 551-560Chapter in book (Refereed)
Place, publisher, year, edition, pages
Studentlitteratur, 2012 Edition: 1
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-6959 (URN)978-91-44-05166-6 (ISBN)
Available from: 2012-12-28 Created: 2012-12-28 Last updated: 2018-01-11Bibliographically approved
Morse, A. & Aktius, M. (2009). Dynamic liquid association: Complex learning without implausible guidance. Neural Networks, 22(7), 875-889
Open this publication in new window or tab >>Dynamic liquid association: Complex learning without implausible guidance
2009 (English)In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 22, no 7, p. 875-889Article in journal (Refereed) Published
Abstract [en]

Simple associative networks have many desirable properties, but are fundamentally limited by their inability to accurately capture complex relationships. This paper presents a solution significantly extending the abilities of associative networks by using an untrained dynamic reservoir as an input filter. The untrained reservoir provides complex dynamic transformations, and temporal integration, and can be viewed as a complex non-linear feature detector from which the associative network can learn. Typically reservoir systems utilize trained single layer perceptrons to produce desired output responses. However given that both single layer perceptions and simple associative learning have the same computational limitations, i.e. linear separation, they should perform similarly in terms of pattern recognition ability. Further to this the extensive psychological properties of simple associative networks and the lack of explicit supervision required for associative learning motivates this extension overcoming previous limitations. Finally, we demonstrate the resulting model in a robotic embodiment, learning sensorimotor contingencies, and matching a variety of psychological data. (C) 2008 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2009
Identifiers
urn:nbn:se:his:diva-7799 (URN)10.1016/j.neunet.2008.10.008 (DOI)000270524500004 ()2-s2.0-69449104579 (Scopus ID)
Available from: 2013-03-28 Created: 2013-03-21 Last updated: 2017-12-06Bibliographically approved
Aktius, M., Nordahl, M. & Ziemke, T. (2007). A Behavior-Based Model of the Hydra, Phylum Cnidaria. In: 9th European Conference, ECAL 2007: Advances in Artificial Life. Paper presented at 9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007. (pp. 1024-1033). Springer Berlin/Heidelberg
Open this publication in new window or tab >>A Behavior-Based Model of the Hydra, Phylum Cnidaria
2007 (English)In: 9th European Conference, ECAL 2007: Advances in Artificial Life, Springer Berlin/Heidelberg, 2007, p. 1024-1033Conference paper, Published paper (Refereed)
Abstract [en]

Behavior-based artificial systems, e.g. mobile robots, are frequently designed using (various degrees and levels of) biology as inspiration, but rarely modeled based on actual quantitative empirical data. This paper presents a data-driven behavior-based model of a simple biological organism, the hydra. Four constituent behaviors were implemented in a simulated animal, and the overall behavior organization was accomplished using a colony-style architecture (CSA). The results indicate that the CSA, using a priority-based behavioral hierarchy suggested in the literature, can be used to model behavioral properties like latency, activation threshold, habituation, and duration of the individual behaviors of the hydra. Limitations of this behavior-based approach are also discussed.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, E-ISSN 0302-9743 ; 4648
Keyword
behavior-based modeling, data-driven modeling, hydra, colony-style architecture
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-2091 (URN)10.1007/978-3-540-74913-4_103 (DOI)000250749000103 ()2-s2.0-38049067103 (Scopus ID)978-3-540-74912-7 (ISBN)
Conference
9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007.
Available from: 2008-05-28 Created: 2008-05-28 Last updated: 2018-01-12Bibliographically approved
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